Data Science Project: Data Analyzation For Used Car Listings on Ebay Car Sales Data
we will be working on a data set of used cars from ebay Kleinanzeigen, a classified section of the German ebay website.
The dataset was originally scraped and uploaded to Kaggle. The version of the dataset we are working with is a sample of 50,000 data points, a less-cleaned version of the data.
The data dictionary provided with data is as follows:
dateCrawled- When this ad was first crawled. All field-values are taken from this date.name- Name of the car.seller- Whether the seller is private or a dealer.offerType- The type of listingprice- The price on the ad to sell the car.abtest- Whether the listing is included in an A/B test.vehicleType- The vehicle Type.yearOfRegistration- The year in which which year the car was first registered.gearbox- The transmission type.powerPS- The power of the car in PS.model- The car model name.kilometer- How many kilometers the car has driven.monthOfRegistration- The month in which which year the car was first registered.fuelType- What type of fuel the car uses.brand- The brand of the car.notRepairedDamage- If the car has a damage which is not yet repaired.dateCreated- The date on which the eBay listing was created.nrOfPictures- The number of pictures in the ad.postalCode- The postal code for the location of the vehicle.lastSeenOnline- When the crawler saw this ad last online.
The aim of this project is to clean the data and analyze the included used car listings.
import pandas as pd
import numpy as np
autos = pd.read_csv("autos.csv", encoding = 'LATIN-1')
autos.info()
autos.head()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 50000 entries, 0 to 49999
Data columns (total 20 columns):
dateCrawled 50000 non-null object
name 50000 non-null object
seller 50000 non-null object
offerType 50000 non-null object
price 50000 non-null object
abtest 50000 non-null object
vehicleType 44905 non-null object
yearOfRegistration 50000 non-null int64
gearbox 47320 non-null object
powerPS 50000 non-null int64
model 47242 non-null object
odometer 50000 non-null object
monthOfRegistration 50000 non-null int64
fuelType 45518 non-null object
brand 50000 non-null object
notRepairedDamage 40171 non-null object
dateCreated 50000 non-null object
nrOfPictures 50000 non-null int64
postalCode 50000 non-null int64
lastSeen 50000 non-null object
dtypes: int64(5), object(15)
memory usage: 7.6+ MB
| dateCrawled | name | seller | offerType | price | abtest | vehicleType | yearOfRegistration | gearbox | powerPS | model | odometer | monthOfRegistration | fuelType | brand | notRepairedDamage | dateCreated | nrOfPictures | postalCode | lastSeen | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2016-03-26 17:47:46 | Peugeot_807_160_NAVTECH_ON_BOARD | privat | Angebot | $5,000 | control | bus | 2004 | manuell | 158 | andere | 150,000km | 3 | lpg | peugeot | nein | 2016-03-26 00:00:00 | 0 | 79588 | 2016-04-06 06:45:54 |
| 1 | 2016-04-04 13:38:56 | BMW_740i_4_4_Liter_HAMANN_UMBAU_Mega_Optik | privat | Angebot | $8,500 | control | limousine | 1997 | automatik | 286 | 7er | 150,000km | 6 | benzin | bmw | nein | 2016-04-04 00:00:00 | 0 | 71034 | 2016-04-06 14:45:08 |
| 2 | 2016-03-26 18:57:24 | Volkswagen_Golf_1.6_United | privat | Angebot | $8,990 | test | limousine | 2009 | manuell | 102 | golf | 70,000km | 7 | benzin | volkswagen | nein | 2016-03-26 00:00:00 | 0 | 35394 | 2016-04-06 20:15:37 |
| 3 | 2016-03-12 16:58:10 | Smart_smart_fortwo_coupe_softouch/F1/Klima/Pan... | privat | Angebot | $4,350 | control | kleinwagen | 2007 | automatik | 71 | fortwo | 70,000km | 6 | benzin | smart | nein | 2016-03-12 00:00:00 | 0 | 33729 | 2016-03-15 03:16:28 |
| 4 | 2016-04-01 14:38:50 | Ford_Focus_1_6_Benzin_TÜV_neu_ist_sehr_gepfleg... | privat | Angebot | $1,350 | test | kombi | 2003 | manuell | 0 | focus | 150,000km | 7 | benzin | ford | nein | 2016-04-01 00:00:00 | 0 | 39218 | 2016-04-01 14:38:50 |
Our dataset contains 20 columns, most of which are stored as strings. There are a few columns with null values, but no columns have more than ~20% null values. There are some columns that contain dates stored as strings.
We’ll start by cleaning the column names to make the data easier to work with.
Clean Columns
autos.columns
Index(['dateCrawled', 'name', 'seller', 'offerType', 'price', 'abtest',
'vehicleType', 'yearOfRegistration', 'gearbox', 'powerPS', 'model',
'odometer', 'monthOfRegistration', 'fuelType', 'brand',
'notRepairedDamage', 'dateCreated', 'nrOfPictures', 'postalCode',
'lastSeen'],
dtype='object')
We’ll make a few changes here:
- Change the columns from camelcase to snakecase.
- Change a few wordings to more accurately describe the columns.
autos.columns = ['date_crawled', 'name', 'seller', 'offer_type', 'price',
'ab_test', 'vehicle_type', 'registration_year', 'gearbox',
'power_ps', 'model', 'odometer', 'registration_month',
'fuel_type', 'brand', 'unrepaired_damage', 'ad_created',
'num_photos', 'postal_code', 'last_seen']
autos.head()
| date_crawled | name | seller | offer_type | price | ab_test | vehicle_type | registration_year | gearbox | power_ps | model | odometer | registration_month | fuel_type | brand | unrepaired_damage | ad_created | num_photos | postal_code | last_seen | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2016-03-26 17:47:46 | Peugeot_807_160_NAVTECH_ON_BOARD | privat | Angebot | $5,000 | control | bus | 2004 | manuell | 158 | andere | 150,000km | 3 | lpg | peugeot | nein | 2016-03-26 00:00:00 | 0 | 79588 | 2016-04-06 06:45:54 |
| 1 | 2016-04-04 13:38:56 | BMW_740i_4_4_Liter_HAMANN_UMBAU_Mega_Optik | privat | Angebot | $8,500 | control | limousine | 1997 | automatik | 286 | 7er | 150,000km | 6 | benzin | bmw | nein | 2016-04-04 00:00:00 | 0 | 71034 | 2016-04-06 14:45:08 |
| 2 | 2016-03-26 18:57:24 | Volkswagen_Golf_1.6_United | privat | Angebot | $8,990 | test | limousine | 2009 | manuell | 102 | golf | 70,000km | 7 | benzin | volkswagen | nein | 2016-03-26 00:00:00 | 0 | 35394 | 2016-04-06 20:15:37 |
| 3 | 2016-03-12 16:58:10 | Smart_smart_fortwo_coupe_softouch/F1/Klima/Pan... | privat | Angebot | $4,350 | control | kleinwagen | 2007 | automatik | 71 | fortwo | 70,000km | 6 | benzin | smart | nein | 2016-03-12 00:00:00 | 0 | 33729 | 2016-03-15 03:16:28 |
| 4 | 2016-04-01 14:38:50 | Ford_Focus_1_6_Benzin_TÜV_neu_ist_sehr_gepfleg... | privat | Angebot | $1,350 | test | kombi | 2003 | manuell | 0 | focus | 150,000km | 7 | benzin | ford | nein | 2016-04-01 00:00:00 | 0 | 39218 | 2016-04-01 14:38:50 |
Initial Data Exploration and Cleaning
We’ll start by exploring the data to find obvious areas where we can clean the data.
autos.describe(include = 'all')
| date_crawled | name | seller | offer_type | price | ab_test | vehicle_type | registration_year | gearbox | power_ps | model | odometer | registration_month | fuel_type | brand | unrepaired_damage | ad_created | num_photos | postal_code | last_seen | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 50000 | 50000 | 50000 | 50000 | 50000 | 50000 | 44905 | 50000.000000 | 47320 | 50000.000000 | 47242 | 50000 | 50000.000000 | 45518 | 50000 | 40171 | 50000 | 50000.0 | 50000.000000 | 50000 |
| unique | 48213 | 38754 | 2 | 2 | 2357 | 2 | 8 | NaN | 2 | NaN | 245 | 13 | NaN | 7 | 40 | 2 | 76 | NaN | NaN | 39481 |
| top | 2016-03-11 22:38:16 | Ford_Fiesta | privat | Angebot | $0 | test | limousine | NaN | manuell | NaN | golf | 150,000km | NaN | benzin | volkswagen | nein | 2016-04-03 00:00:00 | NaN | NaN | 2016-04-07 06:17:27 |
| freq | 3 | 78 | 49999 | 49999 | 1421 | 25756 | 12859 | NaN | 36993 | NaN | 4024 | 32424 | NaN | 30107 | 10687 | 35232 | 1946 | NaN | NaN | 8 |
| mean | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 2005.073280 | NaN | 116.355920 | NaN | NaN | 5.723360 | NaN | NaN | NaN | NaN | 0.0 | 50813.627300 | NaN |
| std | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 105.712813 | NaN | 209.216627 | NaN | NaN | 3.711984 | NaN | NaN | NaN | NaN | 0.0 | 25779.747957 | NaN |
| min | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 1000.000000 | NaN | 0.000000 | NaN | NaN | 0.000000 | NaN | NaN | NaN | NaN | 0.0 | 1067.000000 | NaN |
| 25% | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 1999.000000 | NaN | 70.000000 | NaN | NaN | 3.000000 | NaN | NaN | NaN | NaN | 0.0 | 30451.000000 | NaN |
| 50% | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 2003.000000 | NaN | 105.000000 | NaN | NaN | 6.000000 | NaN | NaN | NaN | NaN | 0.0 | 49577.000000 | NaN |
| 75% | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 2008.000000 | NaN | 150.000000 | NaN | NaN | 9.000000 | NaN | NaN | NaN | NaN | 0.0 | 71540.000000 | NaN |
| max | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 9999.000000 | NaN | 17700.000000 | NaN | NaN | 12.000000 | NaN | NaN | NaN | NaN | 0.0 | 99998.000000 | NaN |
Our initial observations:
- There are a number of text columns where all (or nearly all) of the values are the same:
selleroffer_type
- The num_photos column looks odd, we’ll need to investigate this further.
autos["num_photos"].value_counts()
0 50000
Name: num_photos, dtype: int64
It looks like the num_photos column has 0 for every column. We’ll drop this column, plus the other two we noted as mostly one value.
autos = autos.drop(["num_photos", "seller", "offer_type"], axis=1)
There are two columns, price and odometer, which are numeric values with extra characters being stored as text. We’ll clean and convert these to numeric type.
autos["price"] = (autos["price"].str.replace("$", "")
.str.replace(",", "")
.astype(int))
autos["price"].head()
0 5000
1 8500
2 8990
3 4350
4 1350
Name: price, dtype: int64
autos["odometer"] = (autos["odometer"]
.str.replace("km", "")
.str.replace(",", "")
.astype(int))
autos.rename({"odometer":"odometer_km"}, axis = 1, inplace = True)
autos["odometer_km"].head()
0 150000
1 150000
2 70000
3 70000
4 150000
Name: odometer_km, dtype: int64
Exploring Data for Odometer and Price
autos["odometer_km"].value_counts()
150000 32424
125000 5170
100000 2169
90000 1757
80000 1436
70000 1230
60000 1164
50000 1027
5000 967
40000 819
30000 789
20000 784
10000 264
Name: odometer_km, dtype: int64
The values observed in this field are rounded, which might indicate that the sellers had to choose from pre-set options for this field. Additionally, there are more high mileage than low mileage vehicles.
print(autos["price"].unique().shape)
print(autos["price"].describe())
autos["price"].value_counts().head(20)
(2357,)
count 5.000000e+04
mean 9.840044e+03
std 4.811044e+05
min 0.000000e+00
25% 1.100000e+03
50% 2.950000e+03
75% 7.200000e+03
max 1.000000e+08
Name: price, dtype: float64
0 1421
500 781
1500 734
2500 643
1000 639
1200 639
600 531
800 498
3500 498
2000 460
999 434
750 433
900 420
650 419
850 410
700 395
4500 394
300 384
2200 382
950 379
Name: price, dtype: int64
Again, the prices in this column seem rounded, however given there are 2357 unique values in the column, that may just be people’s tendency to round prices on the site.
There are 1,421 cars listed with $0 price - given that this is only 2% of the of the cars, we might consider removing these rows. The maximum price is one hundred million dollars, which seems a lot. Let’s look at the highest and lowest prices further.
autos["price"].value_counts().sort_index(ascending=False).head(20)
99999999 1
27322222 1
12345678 3
11111111 2
10000000 1
3890000 1
1300000 1
1234566 1
999999 2
999990 1
350000 1
345000 1
299000 1
295000 1
265000 1
259000 1
250000 1
220000 1
198000 1
197000 1
Name: price, dtype: int64
autos["price"].value_counts().sort_index(ascending=True).head(20)
0 1421
1 156
2 3
3 1
5 2
8 1
9 1
10 7
11 2
12 3
13 2
14 1
15 2
17 3
18 1
20 4
25 5
29 1
30 7
35 1
Name: price, dtype: int64
There are a number of listings with prices below $30, including about 1,500 at $0. There are also a small number of listings with very high values, including 14 at around or over $1 million.
Given that eBay is an auction site, there could legitimately be items where the opening bid is $1. We will keep the $1 items, but remove anything above $350,000 since it seems that prices increase steadily to that number and then jump up to less realistic numbers.
autos = autos[autos["price"].between(1, 351000)]
autos["price"].describe()
count 48565.000000
mean 5888.935591
std 9059.854754
min 1.000000
25% 1200.000000
50% 3000.000000
75% 7490.000000
max 350000.000000
Name: price, dtype: float64
Exploring the date columns
There are a number of columns with date information:
date_crawledregistration_monthregistration_yearad_createdlast_seen
These are a combination of dates that were crawled, and dates with meta-information from the crawler. The non-registration dates are stored as strings.
We’ll explore each of these columns to learn more about the listings.
autos[['date_crawled', 'ad_created', 'last_seen']][0:5]
| date_crawled | ad_created | last_seen | |
|---|---|---|---|
| 0 | 2016-03-26 17:47:46 | 2016-03-26 00:00:00 | 2016-04-06 06:45:54 |
| 1 | 2016-04-04 13:38:56 | 2016-04-04 00:00:00 | 2016-04-06 14:45:08 |
| 2 | 2016-03-26 18:57:24 | 2016-03-26 00:00:00 | 2016-04-06 20:15:37 |
| 3 | 2016-03-12 16:58:10 | 2016-03-12 00:00:00 | 2016-03-15 03:16:28 |
| 4 | 2016-04-01 14:38:50 | 2016-04-01 00:00:00 | 2016-04-01 14:38:50 |
The first 10 characters in each column represent the day. For all the columns above, let’s calculate the distribution of values as percentages instead of counts.
(autos["date_crawled"]
.str[:10]
.value_counts(normalize = True, dropna = False)
.sort_index()
)
2016-03-05 0.025327
2016-03-06 0.014043
2016-03-07 0.036014
2016-03-08 0.033296
2016-03-09 0.033090
2016-03-10 0.032184
2016-03-11 0.032575
2016-03-12 0.036920
2016-03-13 0.015670
2016-03-14 0.036549
2016-03-15 0.034284
2016-03-16 0.029610
2016-03-17 0.031628
2016-03-18 0.012911
2016-03-19 0.034778
2016-03-20 0.037887
2016-03-21 0.037373
2016-03-22 0.032987
2016-03-23 0.032225
2016-03-24 0.029342
2016-03-25 0.031607
2016-03-26 0.032204
2016-03-27 0.031092
2016-03-28 0.034860
2016-03-29 0.034099
2016-03-30 0.033687
2016-03-31 0.031834
2016-04-01 0.033687
2016-04-02 0.035478
2016-04-03 0.038608
2016-04-04 0.036487
2016-04-05 0.013096
2016-04-06 0.003171
2016-04-07 0.001400
Name: date_crawled, dtype: float64
(autos["date_crawled"]
.str[:10]
.value_counts(normalize = True, dropna = False)
.sort_values()
)
2016-04-07 0.001400
2016-04-06 0.003171
2016-03-18 0.012911
2016-04-05 0.013096
2016-03-06 0.014043
2016-03-13 0.015670
2016-03-05 0.025327
2016-03-24 0.029342
2016-03-16 0.029610
2016-03-27 0.031092
2016-03-25 0.031607
2016-03-17 0.031628
2016-03-31 0.031834
2016-03-10 0.032184
2016-03-26 0.032204
2016-03-23 0.032225
2016-03-11 0.032575
2016-03-22 0.032987
2016-03-09 0.033090
2016-03-08 0.033296
2016-04-01 0.033687
2016-03-30 0.033687
2016-03-29 0.034099
2016-03-15 0.034284
2016-03-19 0.034778
2016-03-28 0.034860
2016-04-02 0.035478
2016-03-07 0.036014
2016-04-04 0.036487
2016-03-14 0.036549
2016-03-12 0.036920
2016-03-21 0.037373
2016-03-20 0.037887
2016-04-03 0.038608
Name: date_crawled, dtype: float64
Looks like the site was crawled daily over roughly a one month period in March and April 2016. The distribution of listings crawled on each day is roughly uniform.
(autos["last_seen"]
.str[:10]
.value_counts(normalize=True, dropna=False)
.sort_index()
)
2016-03-05 0.001071
2016-03-06 0.004324
2016-03-07 0.005395
2016-03-08 0.007413
2016-03-09 0.009595
2016-03-10 0.010666
2016-03-11 0.012375
2016-03-12 0.023783
2016-03-13 0.008895
2016-03-14 0.012602
2016-03-15 0.015876
2016-03-16 0.016452
2016-03-17 0.028086
2016-03-18 0.007351
2016-03-19 0.015834
2016-03-20 0.020653
2016-03-21 0.020632
2016-03-22 0.021373
2016-03-23 0.018532
2016-03-24 0.019767
2016-03-25 0.019211
2016-03-26 0.016802
2016-03-27 0.015649
2016-03-28 0.020859
2016-03-29 0.022341
2016-03-30 0.024771
2016-03-31 0.023783
2016-04-01 0.022794
2016-04-02 0.024915
2016-04-03 0.025203
2016-04-04 0.024483
2016-04-05 0.124761
2016-04-06 0.221806
2016-04-07 0.131947
Name: last_seen, dtype: float64
The crawler recorded the date it last saw any listing, which allows us to determine on what day a listing was removed, presumably because the car was sold.
The last three days contain a disproportionate amount of ‘last seen’ values. Given that these are 6-10x the values from the previous days, it’s unlikely that there was a massive spike in sales, and more likely that these values are to do with the crawling period ending and don’t indicate car sales.
print(autos["ad_created"].str[:10].unique().shape)
(autos["ad_created"].str[:10]
.value_counts(normalize =True, dropna = False)
.sort_index()
)
(76,)
2015-06-11 0.000021
2015-08-10 0.000021
2015-09-09 0.000021
2015-11-10 0.000021
2015-12-05 0.000021
2015-12-30 0.000021
2016-01-03 0.000021
2016-01-07 0.000021
2016-01-10 0.000041
2016-01-13 0.000021
2016-01-14 0.000021
2016-01-16 0.000021
2016-01-22 0.000021
2016-01-27 0.000062
2016-01-29 0.000021
2016-02-01 0.000021
2016-02-02 0.000041
2016-02-05 0.000041
2016-02-07 0.000021
2016-02-08 0.000021
2016-02-09 0.000021
2016-02-11 0.000021
2016-02-12 0.000041
2016-02-14 0.000041
2016-02-16 0.000021
2016-02-17 0.000021
2016-02-18 0.000041
2016-02-19 0.000062
2016-02-20 0.000041
2016-02-21 0.000062
...
2016-03-09 0.033151
2016-03-10 0.031895
2016-03-11 0.032904
2016-03-12 0.036755
2016-03-13 0.017008
2016-03-14 0.035190
2016-03-15 0.034016
2016-03-16 0.030125
2016-03-17 0.031278
2016-03-18 0.013590
2016-03-19 0.033687
2016-03-20 0.037949
2016-03-21 0.037579
2016-03-22 0.032801
2016-03-23 0.032060
2016-03-24 0.029280
2016-03-25 0.031751
2016-03-26 0.032266
2016-03-27 0.030989
2016-03-28 0.034984
2016-03-29 0.034037
2016-03-30 0.033501
2016-03-31 0.031875
2016-04-01 0.033687
2016-04-02 0.035149
2016-04-03 0.038855
2016-04-04 0.036858
2016-04-05 0.011819
2016-04-06 0.003253
2016-04-07 0.001256
Name: ad_created, Length: 76, dtype: float64
There is a large variety of ad_created dates. Most fall within 1-2 months of the listing date, but a few are quite old, with the oldest at around 9 months.
Now, Let’s have a quick glance at registration_year column details.
autos["registration_year"].describe()
count 48565.000000
mean 2004.755421
std 88.643887
min 1000.000000
25% 1999.000000
50% 2004.000000
75% 2008.000000
max 9999.000000
Name: registration_year, dtype: float64
The year that the car was first registered will likely indicate the age of the car. Looking at this column, we note some odd values. The minimum value is 1000, long before cars were invented and the maximum is 9999, many years into the future.
Dealing with Incorrect Registration Year Data
Because a car can’t be first registered before the listing was seen, any vehicle with a registration year above 2016 is definitely inaccurate. Determining the earliest valid year is more difficult. Realistically, it could be somewhere in the first few decades of the 1900s.
One option is to remove the listings with these values. Let’s determine what percentage of our data has invalid values in this column:
(~autos["registration_year"].between(1900, 2016)).sum() / autos.shape[0]
0.038793369710697
Given that this is less than 4% of our data, we will remove these rows.
# Many ways to select rows in a dataframe that fall within a value range for a column.
# Using `Series.between()` is one way.
autos = autos[autos["registration_year"].between(1900, 2016)]
autos["registration_year"].value_counts(normalize=True).head(10)
2000 0.067608
2005 0.062895
1999 0.062060
2004 0.057904
2003 0.057818
2006 0.057197
2001 0.056468
2002 0.053255
1998 0.050620
2007 0.048778
Name: registration_year, dtype: float64
It appears that most of the vehicles were first registered in the past 20 years.
Exploring Price by Brand
autos["brand"].value_counts(normalize = True)
volkswagen 0.211264
bmw 0.110045
opel 0.107581
mercedes_benz 0.096463
audi 0.086566
ford 0.069900
renault 0.047150
peugeot 0.029841
fiat 0.025642
seat 0.018273
skoda 0.016409
nissan 0.015274
mazda 0.015188
smart 0.014160
citroen 0.014010
toyota 0.012703
hyundai 0.010025
sonstige_autos 0.009811
volvo 0.009147
mini 0.008762
mitsubishi 0.008226
honda 0.007840
kia 0.007069
alfa_romeo 0.006641
porsche 0.006127
suzuki 0.005934
chevrolet 0.005698
chrysler 0.003513
dacia 0.002635
daihatsu 0.002506
jeep 0.002271
subaru 0.002142
land_rover 0.002099
saab 0.001649
jaguar 0.001564
daewoo 0.001500
trabant 0.001392
rover 0.001328
lancia 0.001071
lada 0.000578
Name: brand, dtype: float64
German manufacturers represent four out of the top five brands, almost 50% of the overall listings. Volkswagen is by far the most popular brand, with approximately double the cars for sale of the next two brands combined.
There are lots of brands that don’t have a significant percentage of listings, so we will limit our analysis to brands representing more than 5% of total listings.
brand_counts = autos["brand"].value_counts(normalize = True)
common_brands = brand_counts[brand_counts > 0.05].index
print(common_brands)
Index(['volkswagen', 'bmw', 'opel', 'mercedes_benz', 'audi', 'ford'], dtype='object')
Now, we will identify the mean prices for these brands.
brand_mean_prices = {}
for brand in common_brands:
brand_only = autos[autos["brand"] == brand]
mean_price = brand_only["price"].mean()
brand_mean_prices[brand] = int(mean_price)
brand_mean_prices
{'audi': 9336,
'bmw': 8332,
'ford': 3749,
'mercedes_benz': 8628,
'opel': 2975,
'volkswagen': 5402}
We will convert this dictionary to series and then to a dataframe
bmp_series = pd.Series(brand_mean_prices).sort_values(ascending = False)
pd.DataFrame(bmp_series, columns = ["mean_price"])
| mean_price | |
|---|---|
| audi | 9336 |
| mercedes_benz | 8628 |
| bmw | 8332 |
| volkswagen | 5402 |
| ford | 3749 |
| opel | 2975 |
Of the top 5 brands, there is a distinct price gap:
- Audi, BMW and Mercedes Benz are more expensive
- Ford and Opel are less expensive
- Volkswagen is in between - this may explain its popularity, it may be a ‘best of ‘both worlds’ option.
Exploring Mileage Data
We will extract mean_mileage info for these brands and analyze it
brand_mean_mileage = {}
for brand in common_brands:
brand_only = autos[autos["brand"] == brand]
mean_mileage = brand_only["odometer_km"].mean()
brand_mean_mileage[brand] = int(mean_mileage)
brand_mean_mileage
mean_mileage = pd.Series(brand_mean_mileage).sort_values(ascending = False)
brand_info = pd.DataFrame(mean_mileage, columns = ["mean_mileage"])
brand_info
| mean_mileage | |
|---|---|
| bmw | 132572 |
| mercedes_benz | 130788 |
| opel | 129310 |
| audi | 129157 |
| volkswagen | 128707 |
| ford | 124266 |
brand_info["mean_price"] = bmp_series
brand_info
| mean_mileage | mean_price | |
|---|---|---|
| bmw | 132572 | 8332 |
| mercedes_benz | 130788 | 8628 |
| opel | 129310 | 2975 |
| audi | 129157 | 9336 |
| volkswagen | 128707 | 5402 |
| ford | 124266 | 3749 |
The range of car mileages does not vary as much as the prices do by brand, instead all falling within 10% for the top brands. There is a slight trend to the more expensive vehicles having higher mileage, with the less expensive vehicles having lower mileage.
Conclusion
The comparative table for mean mileage and mean price indicates that Volkswagen is a popular item to buy from eBay Kleinanzeigen. It also represents the tentative price for buyers to evaulate.